Expert Systems - Wiley - Impact Factor

Expert Systems - Wiley | 2024 Impact Factor:2.3 | Cite Score:9.1 | Q2

Impact Factor and Journal Rank of Expert Systems

About: Expert Systems: The Journal of Knowledge Engineering covers a wide range of topics related to knowledge engineering and artificial intelligence. The journal publishes papers that explore various aspects of knowledge acquisition, representation, and application, with a focus on constructing systems such as expert systems.
Significance:
Scope: The journal encompasses research on artificial intelligence, software engineering, requirements engineering, human-computer interaction, and other related fields. It emphasizes individual methods and techniques in knowledge engineering and their practical application in developing expert systems and similar knowledge-based systems.
Knowledge Acquisition and Representation: Expert Systems publishes original research articles, reviews, and methodological papers that advance the understanding and application of knowledge acquisition and representation techniques. Contributions highlight innovative approaches to capturing and organizing domain-specific knowledge.
System Construction: The journal focuses on the construction and evaluation of systems that incorporate expert knowledge, such as expert systems. It explores methodologies for developing robust and effective systems that leverage artificial intelligence techniques to solve complex problems.
Interdisciplinary Approach: Articles in Expert Systems often bridge multiple disciplines, integrating insights from knowledge engineering, artificial intelligence, cognitive psychology, and computer science. This interdisciplinary approach facilitates the development of practical solutions that address real-world challenges.
Submission Guidelines:
Types of Papers: Expert Systems welcomes original research papers, methodological studies, surveys, and application papers. Submissions should contribute new insights, methodologies, or empirical findings that advance the field of knowledge engineering and its applications.
Methodological Rigor: Authors are encouraged to provide rigorous methodological descriptions, clear experimental designs, and thorough evaluations of system implementations. The journal values contributions that demonstrate both theoretical advancements and practical relevance.
Impact and Contributions:
Expert Systems: The Journal of Knowledge Engineering plays a crucial role in advancing knowledge engineering and artificial intelligence research. By fostering discussions on knowledge acquisition, representation techniques, and system construction, the journal contributes to the development of intelligent systems that enhance decision-making and problem-solving capabilities across various domains.

Journal Home: Journal Homepage

Editor-in-Chief: Jon Hall

Scope: Expert Systems: The Journal of Knowledge Engineering covers a wide range of topics within the field of knowledge engineering. The journal publishes papers that explore various aspects of artificial intelligence, software engineering, requirements engineering, human-computer interaction, and methodologies for knowledge acquisition and representation. It emphasizes the application of these methods and techniques in the development and evaluation of systems, particularly expert systems.
The journal serves as a comprehensive platform for researchers and practitioners to share innovative research findings, methodologies, and practical applications in knowledge engineering. It welcomes contributions that advance the understanding and implementation of expert systems and related technologies.
Sample Topics Covered:
Artificial Intelligence: Advances in AI techniques applied to knowledge engineering.
Knowledge Acquisition: Methods and tools for acquiring knowledge from domain experts.
Knowledge Representation: Techniques for representing and organizing knowledge in computer systems.
Expert Systems: Development, implementation, and evaluation of expert systems across various domains.
Software Engineering: Methodologies and practices for software development in knowledge-based systems.
Requirements Engineering: Techniques for eliciting, analyzing, and specifying requirements for knowledge-based systems.
Human-Computer Interaction: Studies on interaction design and usability in knowledge engineering applications.
Machine Learning: Applications of machine learning algorithms in knowledge-based systems.
Natural Language Processing: Techniques for understanding and generating natural language in expert systems.
Decision Support Systems: Design and implementation of decision support tools based on expert systems.
Evaluation and Validation: Methods for assessing the performance and reliability of knowledge-based systems.